Sorry, I don't understand your search. ×
Back to Search Start Over

Scalable detection of technically challenging variants through modified next‐generation sequencing

Authors :
Susan Rojahn
Tina Hambuch
Jessika Adrian
Erik Gafni
Alex Gileta
Hannah Hatchell
Britt Johnson
Ben Kallman
Kate Karfilis
Curtis Kautzer
Michael Kennemer
Lloyd Kirk
Daniel Kvitek
Jessica Lettes
Fenner Macrae
Fernando Mendez
Joshua Paul
Maurizio Pellegrino
Ronny Preciado
Jan Risinger
Matthew Schultz
Lindsay Spurka
Sajani Swamy
Rebecca Truty
Nathan Usem
Andrea Velenich
Swaroop Aradhya
Source :
Molecular Genetics & Genomic Medicine, Vol 10, Iss 12, Pp n/a-n/a (2022)
Publication Year :
2022
Publisher :
Wiley, 2022.

Abstract

Abstract Background Some clinically important genetic variants are not easily evaluated with next‐generation sequencing (NGS) methods due to technical challenges arising from high‐ similarity copies (e.g., PMS2, SMN1/SMN2, GBA1, HBA1/HBA2, CYP21A2), repetitive short sequences (e.g., ARX polyalanine repeats, FMR1 AGG interruptions in CGG repeats, CFTR poly‐T/TG repeats), and other complexities (e.g., MSH2 Boland inversions). Methods We customized our NGS processes to detect the technically challenging variants mentioned above with adaptations including target enrichment and bioinformatic masking of similar sequences. Adaptations were validated with samples of known genotypes. Results Our adaptations provided high‐sensitivity and high‐specificity detection for most of the variants and provided a high‐sensitivity primary assay to be followed with orthogonal disambiguation for the others. The sensitivity of the NGS adaptations was 100% for all of the technically challenging variants. Specificity was 100% for those in PMS2, GBA1, SMN1/SMN2, and HBA1/HBA2, and for the MSH2 Boland inversion; 97.8%–100% for CYP21A2 variants; and 85.7% for ARX polyalanine repeats. Conclusions NGS assays can detect technically challenging variants when chemistries and bioinformatics are jointly refined. The adaptations described support a scalable, cost‐effective path to identifying all clinically relevant variants within a single sample.

Details

Language :
English
ISSN :
23249269
Volume :
10
Issue :
12
Database :
Directory of Open Access Journals
Journal :
Molecular Genetics & Genomic Medicine
Publication Type :
Academic Journal
Accession number :
edsdoj.f96c2a3a0d53441093c4dd5a10f72c60
Document Type :
article
Full Text :
https://doi.org/10.1002/mgg3.2072